The SPE Annual Technical Conference and Exhibition (ATCE) brings together thousands of E&P professionals and experts for learning, networking, and collaborating on the collective goals of the industry. A major highlight in 2018 was the Opening General Session focused on "Translating Big Data into Business Results" with key panelists from Shell, Encana, Schlumberger, and Google Cloud. Registration and housing information for the 2019 conference will be available in mid-June.
Abdulhadi, Muhammad (Dialog Group Berhad) | Tran, Toan Van (Dialog Group Berhad) | Chin, Hon Voon (Dialog Group Berhad) | Jacobs, Steve (Halliburton) | Wahid, Muhammad Izad Abdul (PETRONAS) | Usop, Mohammad Zulfiqar (PETRONAS) | Zamzuri, Dzulfahmi (PETRONAS) | Dolah, Khairul Arifin (PETRONAS) | Abdussalam, Khomeini (PETRONAS) | Munandai, Hasim (PETRONAS) | Yusop, Zainuddin (PETRONAS)
Infill Well B-23, which was recently drilled in the CIII-2 reservoir located in the Balingian Province, experienced a rapid pressure and production decline. The production decreased from 2,200 to 600 BLPD within 1 year. Analysis of the permanent downhole gauge (PDG) data revealed that Well B-23 production was actually influenced by two other wells, B-20 and B-18, each located 2,000 ft away. This paper discusses the ensuing analysis and optimization efforts that helped reverse the Well B-23 pressure decline and restored its production to 2,200 BLPD.
Based on the typical causes of rapid production and pressure decline, operators initially believed Well B-23 was located in a small, separate compartment compared to Wells B-18 and B-20. Additionally, the Well B-23 behavior differed significantly from Wells B-18 and B-20. PDG data analysis provided clear evidence of well interference despite the significant distance between the well locations. Changes in the other wells immediately affected the Well B-23 pressure, thus leading to the conclusion that production from Wells B-20 and B-18 impeded the pressure support for Well B-23. To optimize Well B-23 production, Well B-20 was shut in while Well B-18 was produced at a reduced rate because of a mechanical issue.
The optimization initially resulted in more than 500 BOPD incremental oil from Well B-23. The well pressure decline was reversed, with PDG data showing a continuous increase of bottomhole pressure (BHP) despite an increase in the production rate. Subsequently, production was fully restored from 600 to 2,200 BLPD, and reservoir pressure returned to its predrill pressure. Going forward, the optimum withdrawal rate from the CIII-2 reservoir will be determined to ensure maximum oil recovery from both Wells B-18 and B-23. The case study proved the significant benefit of PDG data, which helped identify well interference as the actual cause of the rapid decline in Well B-23, instead of a reservoir or geological issue. Through in-depth analysis and thorough understanding of the reservoir, the operator restored what initially appeared to be a poor well to full production.
This case study shows the clear and strong effect of well interference and highlights how the subsequent results of the optimization effort were rapidly obtained. A comprehensive understanding of the reservoir behavior could not have been achieved at minimum cost without the pair of PDGs installed. The analysis and lessons learned from the Well B-23 PDG data provide valuable insight regarding the impact of well completions to the field of reservoir engineering.
The effectiveness of secondary and tertiary recovery projects depends heavily on the operator's understanding of the fluid flow characteristics within the reservoir. 3D geo-cellular models and finite element/difference-based simulators may be used to investigate reservoir dynamics, but the approach generally entails a computationally expensive and time-consuming workflow. This paper presents a workflow that integrates rapid analytical method and data-analytics technique to quickly analyze fluid flow and reservoir characteristics for producing near "real-time" results. This fast-track workflow guides reservoir operations including injection fluid allocation, well performance monitoring, surveillance, and optimization, and delivers solutions to the operator using a website application on a cloud-based environment. This web-based system employs a continuity governing equation (Capacitance Resistance Modelling, CRM) to analyze inter-well communication using only injection and production data. The analytic initially matches production history to determine a potential time response between injectors and producers, and simultaneously calculates the connectivity between each pair of wells. Based on the inter-well relationships described by the connectivity network, the workflow facilitates what-if scenarios. This workflow is suitable to study the impact of different injection plans, constraints, and events on production estimation, performance monitoring, anomaly alerts, flood breakthrough, injection fluid supply, and equipment constraints. The system also allows automatic injection re-design based on different number of injection wells to guide injection allocation and drainage volume management for flood optimization solutions. A field located in the Midland basin was analyzed to optimize flood recovery efficiency and apply surveillance assistance. The unit consists of 11 injectors and 22 producers. After optimization, a solution delivering a 30% incremental oil production over an 18-month period was derived. The analysis also predicted several instances of early water breakthrough and high water cut, and subsequent mitigation options. This system couples established waterflood analytics, CRM and modern data-analytics, with a web-based deliverable to provide operators with near "real-time" surveillance and operational optimizations.
Plunger lifted, and free-flowing gas wells experience a wide range of issues and operational inefficiencies such as liquid-loading, downhole and surface restrictions, stuck or leaking motor control valves, and metering issues. These issues can lead to extended downtime, equipment failures, and other production inefficiencies. Using data science and machine-learning algorithms, a self-adjusting anomaly detection model considers all sensor data, including the associated statistical behavior and correlations, to parse any underlying issues and anomalies and classifies the potential cause(s). This paper presents the result of a Proof of Concept (PoC) study conducted for a South Texas operator encompassing 50 wells over a three-month period. The results indicate an improvement compared to the operators' visual inspection and surveillance anomaly detection system. The model allows operators to focus their time on solving problems instead of discovering them. This novel approach to anomaly detection improves workflow efficiencies, decreases lease operating expenses (LOE), and increases production by reducing downtime.
Bhardwaj, Nitin (Reliance Industries Limited, Mumbai, India) | Gunasekaran, Karthikeyan (Reliance Industries Limited, Mumbai, India) | Kumar, Ashutosh (Reliance Industries Limited, Mumbai, India) | Dutta, Jayanta (Reliance Industries Limited, Mumbai, India)
In 3D Pore Pressure Modelling workflow, establishing appropriate Normal Compaction Trend (NCT) is not only critical but also requires the maximum extent of human interpretation and geological understanding. If not established appropriately, it can introduce substantial uncertainty in the final pore pressure prediction. Though, statistical algorithm techniques are available to establish it, the authors of this paper have demonstrated that establishing NCT manually based on geological logic and regional pressure understanding is much more reliable technique than pure statistical based approach.
In this paper, authors utilizes two different approaches in establishing Normal Compaction Trend (NCT) for the study area. First, based on pure statistical technique and second, a manual one based on combination of 3D velocity trends and regional geological pressure understanding. The 3D pore pressure volumes generated from the above two separate NCT’s are then checked for their conformance and agreement with the regional pressure data and understanding, including validation with post drill measured pressure data in the study area.
The results and analysis in the study area shows that, establishment of NCT based purely on statistical approach results in higher uncertainty in the 3D pore pressure estimation process. Whereas, manual NCT based on logic results in much more robust, reliable, and regionally consistent 3D pore pressure model with lower uncertainty. In our case study, the average uncertainty in the statistical NCT based 3D Pressures was ranging between 0.8 – 2.3 PPG when compared with actual pressures, while in the case of logic based manual NCT the average uncertainty was less than 1.0 PPG.
This case study indicates that in the offshore areas, particularly in areas where there is transition from shelf to slope to deepwater, it is advisable to use all the regional pressure knowledge and geological understanding in establishing the NCT, rather than adopting only the pure statistical methods.
Bennett, Nicholas (Schlumberger-Doll Research) | Donald, Adam (Schlumberger) | Ghadiry, Sherif (Schlumberger) | Nassar, Mohamed (Schlumberger) | Kumar, Rajeev (Schlumberger Middle East S.A.) | Biswas, Reetam (The University of Texas)
A new sonic-imaging technique uses azimuthal receivers to determine individual reflector locations and attributes, such as the dip and azimuth of formation layer boundaries, fractures, and faults. From the filtered waveform measurements, an automated time pick and event-localization procedure is used to collect possible reflected arrival events. An automated ray-tracing and 3D slowness time coherence (STC) procedure is used to determine the raypath type of the arrival event and the reflector azimuth. The angle of incidence of the reflected arrival is related to the relative dip, and the moveout in 3D across the individual sensors is related to the azimuthal orientation of the reflector. This information is then used to produce a 3D structural map of the reflector, which can be readily used for further geomodeling.
This new technique addresses several shortcomings in the current state-of-the-art sonic-imaging services within the industry. Similar to seismic processing, the current sonic-imaging workflow consists of iteratively testing migration parameters to obtain a 2D image representing a plane in line with the desired receiver array. The image is then interpreted for features, which is often subjective in nature and does not directly provide quantitative results for the discrete reflections. The technique presented here, besides providing appropriate parameter values for the migration workflow, further complements the migration image by providing dip and azimuth for each event that can be used in further downstream boundary or discontinuity characterization.
A field example from the Middle East is presented in which a carbonate reservoir was examined using this technique and subsequently integrated with wellbore images to provide insight to the structural geological setting, which was lacking seismic data due to surface constraints. Structural dips were picked in the lower zone of the main hole and used to update the orientation of stratigraphic formation tops along the well trajectory. 3D surfaces were then created and projected from the main hole to the sidetrack to check for structural conformity. One of the projected surfaces from the main hole matched the expected depth of the formation top in the sidetrack but two were offset due to the possible presence of a fault. This was confirmed by parallel evaluation of the azimuthal sonic-imaging data acquired in the main hole that showed an abrupt change in the relative dip of reflectors above and below the possible fault plane using the 3D STC and ray tracing. Dip patterns from both wells showed a drag effect around the offset formation tops, further confirming the presence of a fault. A comparison of the acquired borehole images pinpointed the depth and orientation of the fault cutting both wells to explain the depth offset of the projected 3D formation top surfaces.
A new drillstring model has been developed that determines the static and dynamic behavior of bottomhole assemblies (BHAs) in 3D wellbores. An attempt at validating this model with field data is presented, and it shows a close agreement between observed and calculated downhole BHA behavior.
Validation tests were conducted using high-frequency downhole data measured within a motor-assisted rotary-steerable BHA. The gathered data were used to verify the calculated mechanical loads, predicted lateral natural frequencies of the BHA, estimated directional performance of the downhole assembly, and the torsional resonance resulting from high-frequency torsional oscillations (HFTOs).
Results from the field tests show a strong correlation between measured and calculated bending-moment values, as well as lateral natural frequencies of the BHA, with an average of 3% error across all data sets. The primary source of error is thought to be borehole spiraling, which is quantified through analysis of the downhole bending-moment data. In addition, the model is shown to provide close estimates of the actual directional performance of both steerable mud motors and rotary-steerable BHAs. However, the directional-calculation vs. -measurement comparison does reveal a need to incorporate a rate-of-penetration (ROP) dependency within the directional-prediction algorithms.
This paper presents novel approaches and comprehensive field case examples on applying water chemistry in reservoir management and production. Systematic field water sampling and analysis, data integration, and water chemistry fingerprinting techniques are utilized for various important applications such as Original Oil In Place (OOIP) estimate, water source identification, prediction/prevention/management of oilfield scale and other water-related production/operation problems. Field case study examples show significant value creation achieved by utilizing water chemistry-based approaches. Results show subsurface water heterogeneity can significantly impact the calculation of OOIP, water sampling and analysis is critical to identify "unexpected" scaling risk at initial water breakthrough and monitor seawater breakthrough ensuring management/treatment in place as needed, systematic water data collection and integration and understanding can be used as a reliable/efficient/cost-effective approach to identify water source/water breakthrough from a new formation zone. Significant value creation was achieved for projects via our novel and systematic water chemistry-based approach discussed in this paper.
Luo, Xianbo (Tianjin Branch of China National Offshore Oil Company) | Li, Jinyi (Tianjin Branch of China National Offshore Oil Company) | Yang, Dongdong (Tianjin Branch of China National Offshore Oil Company) | Shi, Hongfu (Tianjin Branch of China National Offshore Oil Company)
The relative permeability test (RPT) plays an important part in production prediction, the law of water cut increasing analysis, the research on recovery factor and the reservoir numerical simulation. The residual oil saturation is one of the most significant parameters of RPT. While literature on the quality control of RPT is limited, the experimentalists make qualitative judgments on the rationality of the key data estimate based on their own experience. A new method is presented to predict residual oil saturation of light oil reservoirs.
Drilling and workover operations represent a crucial part of a well lifecycle in terms of deliverability and economics. Understanding the underlying phenomena that cause operational anomalies is the stepping stone into early detection and control of undesired events, such as a kick.
The evolution of artificial intelligence and machine learning applications lend itself to well operations, to gain new efficiencies and unveil hidden insightful observations about downhole and surface operating conditions. Incorporating the mechanisms of natural phenomena and big data, retrieved from sources such as logging while drilling (LWD) and measurement while drilling (MWD), into machine learning models, boost capabilities for early detection of operational anomalies, and mitigation of potential negative consequences, while eliminating human-bias.
This paper highlights a novel machine learning model developed to streamline early detection for the operational anomaly of uncontrolled hydrocarbon flow during well operations, such as drilling. The proposed technique detects and classifies the risk level of a kick before it reaches the surface, to extend the safe response time limit. When this method is integrated with LWD data in real-time mode by means of software, an alarm system can be embedded to alert field hands about downhole conditions. This does not only promote safer operations, but also significantly improves the availability and reliability of critical information.
To further fine-tune the accuracy of the predictive model, multiple rounds of cross-validation were executed on the training data. It is evident that training machine learning models allow for more learning through practice. The technique presented shows that big data and machine learning algorithms are powerful tools to uncover hidden information, and enable improvement in operational leadership.